Accelerator and Beam Physics Research Goals and Opportunities

Nagaitsev, Sergei; Huang, Zhirong; Power, John; Vay, J.-L.; Piot, Philippe; Spentzouris, Linda; Rosenzweig, James; Cai, Yunhai; Cousineau, S.; Conde, Manoel

doi:10.2172/1764152

Cited by 10 publications

(14 citation statements)

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“…Machine learning, especially deep learning, has provided powerful tools for accelerator physicists to build fast-prediction surrogate models [3][4][5] and to extract essential information [6][7][8] from large amounts of data in recent years. These machine-learning models can be extremely useful for building virtual accelerators, which are capable of making fast predictions of the behavior of beams [9], assisting accelerator tuning by virtually bringing destructive diagnostics online [4], providing an initial guess of input parameters for model-independent adaptive feedback control algorithms [10,11], and driving modelbased feedback control algorithms [12]. Deep learning is a subfield of machine learning based on artificial neural networks [13].…”

Section: Introductionmentioning

confidence: 99%

High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks

Zhu

Chen²,

Brinker³

et al. 2021

Phys. Rev. Applied

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Modeling of large-scale research facilities is extremely challenging due to complex physical processes and engineering problems. Here, we adopt a data-driven approach to model the longitudinal phase-spacediagnostic beamline at the photoinector of the European XFEL with an encoder-decoder neural-network model. A deep convolutional neural network (decoder) is used to build images, measured on the screen, from a small feature map generated by another neural network (encoder). We demonstrate that the model, trained only with experimental data, can make high-fidelity predictions of megapixel images for the longitudinal phase-space measurement without any prior knowledge of photoinjectors or electron beams. The prediction significantly outperforms existing methods. We also show the scalability and interpretability of the model by sharing the same decoder with more than one encoder, used for different setups of the photoinjector, and propose a pragmatic way to model a facility with various diagnostics and working points. This opens the door to a way of accurately modeling a photoinjector using neural networks and experimental data. The approach can possibly be extended to the whole accelerator and even other types of scientific facility.

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Section: Introductionmentioning

confidence: 99%

High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks

Zhu

Chen²,

Brinker³

et al. 2021

Phys. Rev. Applied

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“…The roadmaps describe a series of near-term, mid-term and long-term research milestones with the final goal of constructing a multi-TeV e+ecollider. Other important research thrusts include the Radiofrequency Accelerator Roadmap [8], Accelerator and Beam Phyics Roadmap [9], Accelerator Stewardship [10], Particle Sources, and Artificial Intelligence and Machine Learning [11].…”

Section: Research Thrustsmentioning

confidence: 99%

U.S. advanced and novel accelerator beam test facilities

Clarke¹,

Esarey²,

Geddes³

et al. 2022

J. Inst.

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Demonstrating the viability of Advanced Accelerator Concepts (AAC) relies on experimental validation. Over the last three decades, the U.S. has maintained a portfolio of advanced and novel accelerator test facilities to support research critical to AAC. The facilities have enabled pioneering developments in a wide variety of beam and accelerator physics, including plasma-wakefield and structure-wakefield acceleration. This paper provides an overview of the current portfolio of U.S. facilities possessing charged particle drive beams with high energies, on the order of tens of joules per pulse, or drive lasers with high peak powers, on the order of a petawatt, and are actively conducting AAC research.

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“…The research community input during the two ABP workshops [3][4][5] indicated the following areas of research are needed to address the above Grand Challenges (GC).…”

Section: Proposed Accelerator and Beam Physics Research Areasmentioning

confidence: 99%

Accelerator and Beam Physics: Grand Challenges and Research Opportunities

Nagaitsev¹,

Shiltsev²,

Valishev³

et al. 2022

Preprint

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Accelerator and beam physics (ABP) is the science of the motion, generation, acceleration, manipulation, prediction, observation and use of charged particle beams. It focuses on fundamental long-term accelerator and beam physics research and development. Accelerator and beam physics research has resulted in important advances in accelerator science, yet support for this research is declining. NSF has terminated its program in Accelerator Science and funding by DOE through GARD and Accelerator Stewardship has been steady or declining. The declining support for accelerator research will slow advances and threaten student training and work-force development in accelerator science. We propose a robust and scientifically challenging program in accelerator and beam physics, which will position the field of US High Energy Physics to be productive and competitive for decades to come.

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Accelerator and Beam Physics Research Goals and Opportunities

Cited by 10 publications

References 0 publications

High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks

High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks

U.S. advanced and novel accelerator beam test facilities

Accelerator and Beam Physics: Grand Challenges and Research Opportunities

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